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Abstract

In the most recent economic downturn, caused by the burst of the housing bubble and the subprime mortgage crisis, the housing market took a direct hit: mortgage default rates were over 10 percent, housing prices fell 35 percent from 2005 to 2009, more than 5 million homes were foreclosed since 2008, and interest rates hit rock-bottom. In response, the U.S. Federal Reserve adopted aggressive expansionary monetary policies to get the economy back on track, some of which are in effect today. In order to really understand the state and direction of the U.S. economy today, we need to better understand the housing market and its effect on the overall economy.

This paper deals in great length with “the state of the housing market,” which needs to be defined. In studying the response of consumption retail sales to changes in regional and national housing market conditions, Moench and Ng (2011) compile a list of regional and national house price and house volume indicators to measure the state of the U.S. housing market. More specifically, regional data includes 14 house price and volume series, while national data contains 17 house price and volume series. Using a hierarchical factor model framework (FAVAR), the authors extract a common housing factor which accurately defines the state of the housing market. The latent state of the housing market can therefore be measured by calculating the number of standard deviations the housing factor is above or below its historic average.

Housing prices and housing volume are two important variables used to represent the current state of the housing market. It has been established, mainly through equity research, that sales of existing homes (which make up the overwhelming majority of total housing sales) are a leading indicator of home improvement sales. Home improvement sales have been shown to be highly correlated with housing demand (the Trefis Team, 2015, by Forbes), making them an important component of aggregate demand in the U.S (with housing roughly a 15.7% share of GDP and home improvement sales a 14% portion of investment and consumption housing spending in 2016, according to the National Association of Home Builders). Further, analyzing correlations and the timing of these key variables may help predict the next turning point in the housing market, by separating demand driven by macroeconomic fundamentals from that of the housing price bubble. Since bubbles ultimately lead to a sudden collapse of the housing market, policy makers can analyze these trends in their early stages in order to make proper offsetting and preventive monetary policy decisions.

The first chapter titled “Housing Prices and Existing Home Sales: A Study of Frequencies and Correlations” studies correlation between housing prices and existing home sales at different frequencies utilizing dynamic correlations and the band-pass filter, coming to the general conclusion that the farther apart the observations, the higher the correlation between these two series, and vice versa. Originally proposed by Akkoyun et al. (2013), this paper focuses more on the housing side of the equation, but its result has important economic implications. As Bernanke (2010) put it, we need to understand whether house price increases are driven by movements in macroeconomic fundamentals or by a housing bubble. Structural Vector Autoregression analysis is applied to study the short-term relationship between housing and economic variables, and shows a positive correlation between existing home sales and home improvement sales, a proxy for housing demand. Analyzing correlations between housing prices and existing home sales at different frequencies can help us study housing trends at shorter and longer time frames.

The second chapter titled “Money Demand and Housing: A Vector Error Correction Analysis for the U.S.”, based on Greiber and Setzer (2007), focuses on money demand and the role of housing, along with other economic variables, in the U.S. money demand equation. By studying impulse response functions from a Vector Error Correction Model, there is more evidence to support the “asset inflation channel” (expansionary monetary policy inflates asset prices, or causality running from monetary developments to the housing market) rather than the “money demand channel” (higher housing prices create greater demand for money, or causality running from the housing market to money). Replacing housing prices with existing home sales generates an even quicker response in home improvement sales to a positive money shock. In addition, extending the data to 2016 allows us to analyze how the financial crisis affected the relationship between these variables.

The third chapter, titled “Existing Home Sales and their Impact on the U.S. Housing Market: Evidence from the Pooled Mean Group Estimation”, uses a panel of fifty U.S. states, and the District of Columbia, over a period of 26 years to study the long-run relationship between existing home sales, 30-year mortgage rates, gross state product, and home improvement sales, a proxy for housing demand. This paper utilizes panel cointegration techniques such as the Westerlund (2007) cointegration test, Pooled Mean Group and Mean Group estimation, long-run deviations from equilibrium, and Granger causality to analyze the relationship between housing and economic variables. These techniques confirm the expected long-run relationship between the variables: a positive effect of existing home sales and gross state product on home improvement sales.

While the three chapters differ in their techniques and methodologies (dynamic correlations, money demand equation, and panel cointegration analysis), there is a unifying theme of studying the housing market and its effect on the overall economy. The chapters use home improvement sales as a leading indicator of housing demand, and show that existing home sales are just as important, if not more important, than housing prices in analyzing housing trends. This theory is popular in equity research and market analysis. For instance, the Trefis Team (2015) by Forbes states that the home improvement industry is highly correlated with the state of the housing market, with existing home sales being one of the most important drivers for the industry. A Bloomberg (2006) article states that home-improvement stores are "particularly sensitive" to existing home sales and a Goldman Sachs (2013) research study claims that home improvement retail sales are driven by rising housing turnover (existing home sales per household) and recovering home prices. To further justify the use of existing home sales over housing prices, Berkovec and Goodman (1996) use the search and matching friction model to show that housing turnover is a better indicator of changes in housing demand than housing prices and that the price-turnover relationship is stronger for lower-frequency data, two significant results.

Through a series of interrelated hypotheses and results, we can see the fundamental role housing plays in our economic framework. The first chapter on correlations and frequencies suggests that by analyzing two related housing indicators, we may miss the bigger picture. Rather, the relationship between the two series can change based on how readily the data become available, and which region of the country is being analyzed. The second chapter highlights the importance of including a housing variable in the standard money demand equation and provides more evidence in support of the asset inflation channel. Finally, the third chapter uses panel estimation techniques to demonstrate the positive relationship between home improvement sales and existing home sales, which can be used to help predict changes in housing demand. Therefore, based on this analysis, the state of the U.S. economy and the state of the housing market are “related phenomena” (Greiber and Setzer, 2007) which should be studied conjointly.

The dissertation is organized as follows. Chapter I contains the essay titled “Housing Prices and Existing Home Sales: A Study of Frequencies and Correlations.” Chapter II contains the essay titled “Money Demand and Housing: A Vector Error Correction Analysis for the U.S.” Chapter III contains the essay titled “Existing Home Sales and their Impact on the U.S. Housing Market: Evidence from the Pooled Mean Group Estimation.”

Recommended Citation

Ayzenberg, Irina, "Analyzing Housing Demand and Its Role in the U.S. Economy" (2018). CUNY Academic Works.https://academicworks.cuny.edu/gc_etds/2696